chore: stub sentiment scoring, drop nltk

Sentiment analysis pulled in nltk + a baked-in vader_lexicon (~50MB),
which inflated the Lambda image and added cold-start cost. Stub
count_sentiments() to a constant 0.5 for now. The TODO in tasks.py
calls out how to revive it.

- remove nltk from requirements
- drop the vader_lexicon download from both Dockerfiles
- drop scripts/download-ntk.py and the install-step Makefile reference

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: Androz2091

Dockerfile.api

@@ -4,15 +4,6 @@ FROM python:3.10-slim-buster
 # Create application directory and move there
 WORKDIR /app
 
-# Install nltk
-RUN pip install --no-cache-dir --prefer-binary nltk
-
-# Copy the script that downloads the vader lexicon module
-COPY scripts/download-ntk.py ./
-
-# Execute that script
-RUN python download-ntk.py
-
 # Copy requirements file from the host to the container
 COPY requirements.txt ./
 

Dockerfile.lambda

@@ -2,17 +2,11 @@
 # handler is selected per-function via the Lambda ImageConfig.Command.
 FROM public.ecr.aws/lambda/python:3.10
 
-# NLTK data lives at a known path so we can ship it with the image and skip
-# any runtime download attempts (which would fail in the VPC anyway).
-ENV NLTK_DATA=/var/task/nltk_data
-
 WORKDIR ${LAMBDA_TASK_ROOT}
 
 COPY requirements.txt ./
 RUN pip install --no-cache-dir -r requirements.txt
 
-RUN python -c "import nltk; nltk.download('vader_lexicon', download_dir='${NLTK_DATA}')"
-
 # Flat layout: src/* sits at the task root so existing top-level imports
 # (`from tasks import ...`) keep working.
 COPY src/ ${LAMBDA_TASK_ROOT}/

Makefile

@@ -14,7 +14,6 @@ install:
 	uv venv --python 3.10
 	. .venv/bin/activate
 	uv pip install -r requirements.txt
-	uv run ./scripts/download-ntk.py
 
 dev:
 	. .venv/bin/activate

requirements.txt

@@ -22,7 +22,6 @@ limits==3.5.0
 markdown-it-py==3.0.0
 MarkupSafe==2.1.2
 mdurl==0.1.2
-nltk==3.9.1
 numpy==1.24.3
 ordered-set==4.1.0
 orjson==3.9.15

scripts/download-ntk.py

@@ -46,18 +46,15 @@
 
 from wh import send_internal_notification
 
-from nltk.sentiment import SentimentIntensityAnalyzer
-sia = SentimentIntensityAnalyzer()
-
+# TODO: restore real sentiment scoring. Stubbed to a constant so we can
+# drop NLTK + the vader_lexicon download from the Lambda image while we
+# decide whether sentiment is worth the ~50MB of data and the cold-start
+# cost. Re-enable by reinstating SentimentIntensityAnalyzer here and the
+# nltk install step in Dockerfile.lambda.
 def count_sentiments(contents):
-    sentiments = []
-    for content in contents:
-        if not content:
-            continue
-        score = sia.polarity_scores(str(content))["compound"]
-        if score != 0:
-            sentiments.append(score)
-    return sum(sentiments) / len(sentiments) if len(sentiments) > 0 else 0
+    for _ in contents:
+        pass
+    return 0.5
 
 def find_user_root(zip_namelist):
     """